CAMBRIDGE, MA – October 22, 2020—Rapid Reviews: COVID-19 (RR:C19), an open-access overlay journal published by the MIT Press that accelerates peer review of COVID-19-related research preprints, is currently soliciting reviews of the following COVID-19 preprints. These preprints have been selected for review because they have the potential to enhance our understanding of SARS-CoV-2 or have been flagged as potentially misleading. Preprints with two finished reviews should be published within 10-14 days. Additional information or early access to these peer-reviews is available upon request.
Highlights from Rapid Reviews editorial team:
- “Ultrasensitive and selective detection of SARS-CoV-2 using thermotropic liquid crystals and image-based machine learning” by Yang Xu, et al.
Preprint Summary: This study developed a novel diagnostic strategy that uses liquid crystal-conjugated nucleic acid probes to detect SARS-CoV-2 genetic sequences and show 3-base pair mismatches reduce signal by log 7. The authors also develop a diagnostic kit, including machine learning algorithm and diagnostic app to facilitate use in a Point of Care setting.
- “A sew-free origami mask for improvised respiratory protection” by Jonathan Realmuto, et al.
Preprint Summary: An origami mask with adequate filtration efficiency can be made by non-experts using two elastic straps, square piece of filter material, nose clip material (twist tie, paper clip, etc.), and stapler. These masks can be made easily with minimal materials while providing appropriate filtration as tested using a mannequin based test for air flow and filtration.
- “Hitting the diagnostic sweet spot: Point-of-care SARS-CoV-2 salivary antigen testing with an off-the-shelf glucometer” by Naveen Singh, et al.
Preprint Summary: A SARS-CoV-2 salivary antigen assay that uses a glucometer as readout for point-of-care diagnosis. The test is based on low-cost($3.20/test) reagents and off-the-shelf glucometer, and was able to deliver 100% sensitivity and 100% specificity within one hour as benchmarked by RT-qPCR.
- “SARS-CoV-2 viral budding and entry can be modeled using virus-like particles” by Caroline Plescia, et al.
Preprint Summary: This study documents purification and assembly of SARS-CoV-2 Virus-like particles (VLPs). They study viral budding and entry in the presence of potential inhibitory drugs, in BSL-2 conditions.
- “SARS-CoV-2 antibody responses in patients with aggressive haematological malignancies” by Jenny O’Nions, et al.
Preprint Summary: The majority (8/9) of Patients with Hematologic Malignancy (PHM) with confirmed SARS-CoV-2 infection seroconverted and developed antibodies to the major SARS-CoV-2 antigens (S1 and N) with most (6/8) produced neutralizing antibody responses. Furthermore, the dynamics of antibody responses were broadly similar to that reported for the general population, except for a possible delay to seroconversion.
- “COVID-19 classification of x-ray images using deep neural networks” by Elisha Goldstein, et al.
Preprint Summary: Deep Neural Networks can be used to reliably classify CXR images as COVID-19 positive or negative. The model achieved 89.7% accuracy and 87.1% sensitivity (testing 15% of images) in classification of COVID-19 and area under receiver operating characteristic curve of 0.95. This could be a tool used to rapidly identify and isolate positive COVID-19 patients, which would contribute to contain the disease.
- “COVID-19 infection may cause thyroid dysfunction” by Hongmei Zhang.
Preprint Summary: Relative to the control, COVID+ patients had lower serum levels of T3 and TSH and higher levels of T4. Thyroid dysfunction in COVID+ patients manifested often as non-thyroidal illness syndrome (NTIS). The FT3/FT4 ratio was negatively correlated with COVID-19 death in the multivariate logistic regression analysis. AUC for the FT3/FT4 ratio was 0·716 (p =0·000, 95%CI=0·633-0·799).
RR:C19 is published by the MIT Press and the editorial offices are located at UC Berkeley, headed by editor-in-chief Stefano M. Bertozzi, Professor of Health Policy and Management and Dean Emeritus of the School of Public Health at University of California Berkeley. The journal is funded by a grant from the Patrick J. McGovern Foundation and hosted on PubPub, an open-source publishing platform from the Knowledge Futures Group.
To learn more about this project and its editorial board, or to sign up for future news and alerts, visit rapidreviewscovid19.mitpress.mit.edu
Associate Director of Publicity
Fortier Public Relations
Kate Silverman Wilson
Community and Resource Development Associate
The MIT Press
About the MIT Press
Established in 1962, the MIT Press is one of the largest and most distinguished university presses in the world and a leading publisher of books and journals at the intersection of science, technology, art, social science, and design. MIT Press books and journals are known for their intellectual daring, scholarly standards, interdisciplinary focus, and distinctive design.
About the UC Berkeley School of Public Health
For 75 years and counting, the UC Berkeley SPH has been dedicated to making a transformative impact on the health of populations through its values of health as a right, strength through diversity, think forward, and impact first. To eliminate inequity and injustice that affects the health and dignity of all people, SPH is committed to radical public health collaborations that challenge conventional thinking, leverage technology, and build bridges between research, public policy, education, and action.
About the Patrick J. McGovern Foundation
The Patrick J. McGovern Foundation is dedicated to improving lives globally with technology, data and AI. The Foundation is the legacy of IDG founder Patrick J. McGovern, who believed in the potential for technology to democratize information, improve the human condition and advance social good
About the Knowledge Futures Group
The Knowledge Futures Group, a nonprofit originally founded as a partnership between the MIT Press and MIT Media Lab, builds and sustains technology for the production, curation, and preservation of knowledge in service of the public good.
Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system.